A split kernel adaptive filtering architecture for nonlinear acoustic echo cancellation
A split kernel adaptive filtering architecture for nonlinear acoustic echo cancellation
复制标题
用于非线性声学回声消除的分裂内核自适应滤波架构
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Comminiello
中科院分区：
文献类型：
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作者：
S. V. Vaerenbergh;L. A. Azpicueta;D. Comminiello
针对非线性声学回波抵消(NAEC)问题，提出了一种基于核方法的参数线性(LIP)非线性滤波器。为此，我们定义了一个基于并行方案的框架，其中任何基于核的自适应滤波器(KAF)都可以有效地结合在一起。该结构由一个分支上的经典自适应滤波器和另一个分支上的核自适应滤波器组成，前者致力于估计回波路径的线性部分，后者用于模拟回波路径中的非线性反弹。此外，我们还提出了一种低复杂度、参数很少的最小均方(LMS)KAF，并将其应用于并行体系结构中。最后，我们在实际的NAEC场景中，针对KAF的不同选择，演示了所提出的方案的有效性。
We propose a new linear-in-the-parameters (LIP) nonlinear filter based on kernel methods to address the problem of nonlinear acoustic echo cancellation (NAEC). For this purpose we define a framework based on a parallel scheme in which any kernel-based adaptive filter (KAF) can be incorporated efficiently. This structure is composed of a classic adaptive filter on one branch, committed to estimating the linear part of the echo path, and a kernel adaptive filter on the other branch, to model the nonlinearities rebounding in the echo path. In addition, we propose a novel low-complexity least mean square (LMS) KAF with very few parameters, to be used in the parallel architecture. Finally, we demonstrate the effectiveness of the proposed scheme in real NAEC scenarios, for different choices of the KAF.